A review of waiting times policies in 13 OECD countries
Bibliographic record
Abstract
This chapter reviews various policy tools that countries have used to tackle excessive waiting times in 13 countries: Australia, Canada, Denmark, Finland, Ireland, Italy, Netherlands, New Zealand, Norway, Portugal, Spain, Sweden and the United Kingdom. The most common policy is some form of maximum waiting time guarantee. Increasingly, such guarantees are backed with targets set for providers and sanctions if these targets are not met. The guarantees often go hand-in-hand with choice, competition and an increase in supply (in the public and/or the private sector). These policies have generally been successful in bringing down waiting times. In contrast, most attempts to increase supply temporarily in order to decrease waiting times have had only a limited effect. A better approach may be to condition increases in supply on simultaneous reductions in waiting times. Demand-side policies attempt to define more rigorous clinical thresholds for treatment. However, it has proved difficult to implement such thresholds. The most promising approaches link waiting time guarantees to different categories of clinical need, also referred to as waiting time prioritisation. An alternative demand-side approach is to encourage private health insurance to shift demand from the public to the private sector, though this has generally not proven successful in reducing waiting times.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".